Dennis Fok

The Short- and Long-Term Impact of an Assortment Reduction on Category Sales

In a collaborative study with a major Dutch retailer, the authors assess the short- and long-term effects of a 25% item reduction on category sales. On an aggregate level, a major assortment reduction can lead to substantive short-term category sales losses but only a weak negative long-term category sales effect. Short-term category sales losses are caused mainly by fewer category purchases by former buyers of delisted detergent items. However, the results also show that the assortment reduction attracts new category buyers. These new buyers partially offset the sales losses among former buyers of the delisted items. The collection of supplemental process data on assortment perceptions and actual search time in the test stores before and after the assortment reduction provides evidence that delisting results in an increase in perceived search efficiency and a decrease in actual search time.

A Hierarchical Bayes Error Correction Model to Explain Dynamic Effects of Price Changes

The authors put forth a sales response model to explain the differences in immediate and dynamic effects of promotional prices and regular prices on sales. The model consists of a vector autoregression that is rewritten in error correction format, which allows the authors to disentangle the immediate effects from the dynamic effects. In a second level of the model, the immediate price elasticities, the cumulative promotional price elasticity, and the long-term regular price elasticity are correlated with various brand-specific and category-specific characteristics. The model is applied to seven years of data on weekly sales of 100 different brands in 25 product categories. The authors find many significant moderating effects on the elasticity of price promotions. Brands in categories that are characterized by high price differentiation and that constitute a lower share of budget are less sensitive to price discounts. Deep price discounts increase the immediate price sensitivity of customers. The authors also find significant effects for the cumulative elasticity. The immediate effect of a regular price change is often close to zero. The long-term effect of such a regular price decrease usually amounts to an increase in sales. This is especially true in categories that are characterized by a large price dispersion and frequent price promotions and for hedonic, nonperishable products.

Modeling Global Spillover of New Product Takeoff

This article examines the global spillover of foreign product introductions and takeoffs on a focal countrys time to takeoff, using a novel data set of penetration data for eight high-tech products across 55 countries. It shows how foreign clout, the susceptibility to foreign influences, and intercountry distances affect global spillover patterns. The authors find that foreign takeoffs, but not foreign introductions, accelerate a focal countrys time to takeoff. The larger the country, the higher its economic wealth, and the more it exports, the more clout it has in the global spillover process. In contrast, the poorer the country, the more tourists it receives, and the higher its population density, the more susceptible it is to global spillover effects. Cross-country spillover effects are stronger the closer the countries are to one another, both geographically and economically, but not necessarily in terms of culture. The model the authors develop also quantifies the spillover between each country pair, allowing it to be asymmetric.

Interaction between Shelf Layout and Marketing Effectiveness and its Impact on Optimizing Shelf Arrangements

In this paper, we propose and operationalize a new method for optimizing shelf arrangements. We show that there are important dependencies between the layout of the shelf and stock-keeping unit (SKU) sales and marketing effectiveness. The importance of these dependencies is further shown by the substantive profit gains we obtain with our proposed shelf optimization approach. The basis of our model is a standard sales equation that explains sales using item-specific marketing effect parameters and intercepts. In a Hierarchical Bayes (HB) fashion, we augment this model with a second layer that relates the effect parameters to shelf and SKU descriptors. We also take into account potential endogeneity of facings. After estimating the parameters of the two-level model using Bayesian methodology, we carefully investigate the dependencies of SKU sales and SKU marketing effectiveness on the shelf layout. Next, we search for the shelf arrangement that maximizes the expected total profit using simulated annealing (SA). We appear to be able to increase profits for all the stores analyzed, and our approach appears to outperform well-known rules of thumb.

Econometric analysis of the market share attraction model

Market share attraction models are useful tools for analyzing competitive structures. The models can be used to infer cross-effects of marketing-mix variables, but also the own effects can be adequately estimated while conditioning on competitive reactions. Important features of attraction models are that they incorporate that market shares sum to unity and that the market shares of individual brands are in between 0 and 1. Next to analyzing competitive structures, attraction models are also often considered for forecasting market shares. The econometric analysis of the market share attraction model has not received much attention. Topics as specification, diagnostics, estimation and forecasting have not been thoroughly discussed in the academic marketing literature. In this chapter we go through a range of these topics, and, along the lines, we indicate that there are ample opportunities to improve upon present-day practice.

Moderating Factors of Immediate, Gross, and Net Cross-Brand Effects of Price Promotions

This article examines cross-price promotional effects in a dynamic context. Among other things, we investigate whether previously established findings hold when consumer and competitive dynamics are taken into account. Five main influential effects asymmetric price effect, neighborhood price effect, asymmetric share effect, neighborhood share effect, and private label versus national brand asymmetry appear jointly in the second layer of a pooled HB-VEC-VARX model, together with brand-and category-specific variables. This study tests the relative importance of these key factors across three scenarios: with no market dynamics, when only consumer dynamics are considered, and when competitive reactions are also taken into account. The results confirm all five influential effects, even if they are jointly estimated, and consumer and competitive dynamics are taken into account. National brand/private label asymmetry has the strongest influence on the cross-price promotional effects and becomes significantly stronger when consumer and competitive dynamics are taken into account. Dynamic consumer responses and competitive reactions both affect cross-brand price elasticities, and contrary to expectations, competitive reactions accumulate rather than diminish cross-price elasticities. Preemptive switching does occur; i.e., a brands promotion in period t hurts a competitors sales in subsequent periods. Our findings are based on an extensive data set. To attain generalizable results, we analyze 33 categories in five stores---that is, 165 store/category combinations.

Modeling Seasonality in New Product Diffusion

textabstractWe propose a method to include seasonality in any diffusion model that has a closed-form solution. The resulting diffusion model captures seasonality in a way that naturally matches the original diffusion models pattern. The method assumes that additional sales at seasonal peaks are drawn from previous or future periods. This implies that the seasonal pattern does not influence the underlying diffusion pattern. The model is compared with alternative approaches through simulations and empirical examples. As alternatives, we consider the standard Generalized Bass Model (GBM) and the basic Bass Model, which ignores seasonality. One of the main findings is that modeling seasonality in a GBM generates good predictions but gives biased estimates. In particular, the market potential parameter is underestimated. Ignoring seasonality in cases where data of the entire diffusion period are available gives unbiased parameter estimates in most relevant scenarios. However, ignoring seasonality leads to biased parameter estimates and predictions when only part of the diffusion period is available. We demonstrate that our model gives correct estimates and predictions even if the full diffusion process is not yet available.

Model-Based Purchase Predictions for Large Assortments

Being able to accurately predict what a customer will purchase next is of paramount importance to successful online retailing. In practice, customer purchase history data is readily available to make such predictions, sometimes complemented with customer characteristics. Given the large assortments maintained by online retailers, scalability of the prediction method is just as important as its accuracy. We study two classes of models that use such data to predict what a customer will buy next: A novel approach that uses latent Dirichlet allocation (LDA), and mixtures of Dirichlet-Multinomials (MDM). A key benefit of a model-based approach is the potential to accommodate observed customer heterogeneity through the inclusion of predictor variables. We show that LDA can be extended in this direction while retaining its scalability. We apply the models to purchase data from an online retailer and contrast their predictive performance with that of a collaborative filter and a discrete choice model. Both LDA and MDM outperform the other methods. Moreover, LDA attains performance similar to that of MDM while being far more scalable, rendering it a promising approach to purchase prediction in large assortments.

A New Multivariate Product Growth Model

To examine cross-country diffusion of new products, marketing researchers have to rely on a multivariate product growth model. We put forward such a model, and show that it is a natural extension of the original Bass (1969) model. We contrast our model with currently in use multivariate models and we show that inference is much easier and interpretation is straightforward. In fact, parameter estimation can be done using standard commercially available software. We illustrate the benefits of our model relative to other models in simulation experiments. An application to a three-country CD sales series shows the merits of our model in practice.

Parameter estimation in multivariate logit models with many binary choices

ABSTRACT Multivariate Logit models are convenient to describe multivariate correlated binary choices as they provide closed-form likelihood functions. However, the computation time required for calculating choice probabilities increases exponentially with the number of choices, which makes maximum likelihood-based estimation infeasible when many choices are considered. To solve this, we propose three novel estimation methods: (i) stratified importance sampling, (ii) composite conditional likelihood (CCL), and (iii) generalized method of moments, which yield consistent estimates and still have similar small-sample bias to maximum likelihood. Our simulation study shows that computation times for CCL are much smaller and that its efficiency loss is small.